The availability of on-board electronics and in-vehicle information systems has demanded the development of more intelligent vehicles. One such important intelligence is the possibility to evaluate the driving danger level to prevent potential driving risks. Although protocols to measure the driver's workload have been developed by both the government and industry, such as eye-glance and on-road metrics, they have been criticized as too costly and difficult to obtain. In addition, existing uniform heuristics such as the 15-Second for Total Risk Time, do not account for the changes in individual driver's and vehicle's environment. Hence understanding the driver's and the vehicle's frustration to prevent potential driving risks has been listed by many international companies as one of the key area for improving intelligent transportation systems.
In the past decades, most reported work sought to discovery effective physiological and bio-behavioral measures to detect the diminished driver vigilance level due to stress, fatigue or drowsiness to prevent potential risks. The most accurate techniques for monitoring human vigilance level is based on physiological features like brain waves, heart rate, blood volume pulse and respiration. Examples based on physiological measure include the ASV (Advanced Safety Vehicle) system and the SmartCar project from MIT. However, acquiring physiological data is intrusive because some electrodes or sensors must be attached to the drivers, which causes annoyance to them. For example, to obtain the electroencephalograph signal (EEG) for “Mind Switch” technique, a head-band device must embedded with electrodes to make contact with the driver's scalp so as to measure the brain waves. Good results have also been reported with techniques that monitor pupil response, eye blinking/closure/gaze and eyelid/face/head movement using head-mounted devices. These techniques, though less intrusive, are still not practically acceptable.
To develop non-intrusive driving risk monitoring and alerting system, two sets of features are available. The first is to monitor the drivers' visual behavior using remote camera(s) and apply computer vision techniques to extract features that are correlated to their fatigue state. For example, the driver's head pose and face direction were recognized from multiple camera using 3D stereo matching or from single camera using template matching. In one head/eye tracking system, a single camera monitors driver's drowsiness level. To cope with different lighting condition, infrared LED is used for illumination. To reduce uncertainty or ambiguity from single visual cue, multiple visual features could be utilized to improve accuracy and reliability.
However, systems relying on visual cues may exhibit difficulty when the required visual features cannot be acquired accurately or reliably. For example, drivers with sun glasses could pose serious problem to those techniques based on detecting eye characteristics. Although multiple visual cues can be combined systematically, how to select suitable model to fuse these features to improve the overall accuracy remains challenging. Hence another set of non-intrusive features based on the vehicle's dynamic state have been examined, such as lateral position, steering wheel movement, throttle acceleration/break deceleration, etc. In fact, the vehicle' dynamic state is a direct reflection of the state of the driving, while researches focusing on modeling driver's vigilance have assumed the close correlation between fatigue/stress and driving danger. Hence many researchers used this set of features for driver safety monitoring. Some important examples include the Spanish TCD (Tech. Co. Driver) project and the ASV system. However, although the extraction of these vehicle's dynamic parameters can be blind to the driver, it is argued that their quality is subjected to the vehicle type, driver experience, geometric characteristics, state of the road, etc limitations.
On the other hand, from a pattern recognition point of view, the task of predicting current driving danger level can be regarded as an anomaly detection problem. Anomaly detection has many important real-world applications, ranging from security, finance, biology, manufacturing and astrophysics, each domain with a huge volume of literature. To detect anomaly in simple scenario, the rule-based methods can be used where any violation of the rule(s) is regarded as an anomaly. For example, a complex rule-based approach has been used to characterize the anomalous pattern for disease outbreak detection. Each rule is carefully evaluated using Fisher's Exact Test and a randomization test. For more complex anomaly detection task such as driver danger level prediction in this paper, defining rules becomes extreme difficult. Hence many other researches applied statistical modeling methods for anomaly detection. For example, the Fisher projection and linear classifier can model the low/medium/high stress level using physiological features. A newly coming data was classified using the Bayesian approach. In another example, a two-category classifier using SVM classifies the incoming data as normal or anomalous. However, these methods overlooked the spatial correlation between features. To cope with the limitation, the Bayesian Network can fuse different features for inference.